Meta-Learning Extractors for Music Source Separation
By David Samuel, Aditya Ganeshan, Jason Naradowsky. 2020
68 citations recorded by the source corpus.
We propose a hierarchical meta-learning-inspired model for music source separation (Meta-TasNet) in which a generator model is used to predict the weights of individual extractor models. This enables efficient parameter-sharing, while still allowing for instrument-specific parameterization. Meta-TasNet is shown to be more effective than the models trained independently or in a multi-task setting, and achieve performance comparable with state-of-the-art methods. In comparison to the latter, our extractors contain fewer parameters and have faster run-time performance. We discuss important architectural considerations, and explore the costs and benefits of this approach.
Subfields: Artificial Intelligence, Signal Processing
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
In Artificial Intelligence
- Alexandre D'efossez
- Yoshua Bengio
- L. Eon Bottou
- Jason Aaron Edward Weston
- Francis R. Bach
- Nicolas Usunier
- Martin Jaggi
- Mohit Bansal
In Signal Processing
- Alexandre D'efossez
- Francis R. Bach
- Naoya Takahashi
- L. Eon Bottou
- Nicolas Usunier
- Yuki Mitsufuji
- Yossi Adi
- Gabriel Synnaeve
Citations in the corpus
- Music Source Separation in the Waveform Domain (2019, methodology): “We also compare to the more recent variant of DPRNN by Nachmani et al. [2020], Meta-Tasnet [Samuel et al., 2020], and Tasnet trained on extra data [Lancaster and Souviraà-Labastie, 2020].”
- D3Net: Densely connected multidilated DenseNet for music source separation (2020, methodology): “The network outputs are used to calculate the multichannel Wiener filter (MWF) to obtain the final separations, as commonly performed in frequency domain audio source separation methods [3, 8, 10, 11].”